90 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" positions at Universidade de Coimbra
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. The publicity rules are available on the FCT website, as well as on the websites of the funding Operational Programmes, when applicable. The use of the FCT logo available at http://www.fct.pt/logotipos/ is
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jury decisions must be justified, recorded in minutes, and published on https://apply.uc.pt/ . VII.VII - Jury Composition: President: Licínio Gomes Roque Effective Members: Jorge Carlos dos Santos
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engineering Engineering » Mechanical engineering Engineering » Other Researcher Profile First Stage Researcher (R1) Positions Bachelor Positions Application Deadline 8 Apr 2026 - 23:59 (Europe/Lisbon) Country
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engineering Engineering » Other Engineering » Mechanical engineering Engineering » Chemical engineering Engineering » Industrial engineering Researcher Profile First Stage Researcher (R1) Positions Bachelor
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necessary for the service to be provided outside the usual workplace. Where to apply Website https://app-4.apply.uc.pt Requirements Research FieldOtherEducation LevelPhD or equivalent Additional Information
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- Work Plan / Goals to be achieved: In the first phase, samples will be produced using the WAAM process, which will subsequently be machined to obtain specimens with appropriate dimensions and geometries
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-of-the-art models for computer vision based on Machine Learning. Work plan: - Analysis and study of existing resources. - Analysis of the state of the art in universal adversarial attacks on computer vision
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-of-the-art models for computer vision based on Machine Learning. - Analysis and Study of existing resources; - Analysis of the state of the art in adversarial attacks and adversarial training and their
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, recorded in minutes, and published on https://apply.uc.pt/ . VII.IX -Jury Composition: President: John Griffith Jones Effective Members: Bruno José Fernandes Oliveira Manadas;Ivan Daniel dos Santos Martins
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objectives: 1 – Development of a tool for identifying operating regimes using machine learning techniques. 2 – Development of a tool for identifying the causes of process eco-efficiency degradation using